Much research has gone into Bayesian optimization for continuous design spaces using Gaussian processes (GP) as a surrogate model and efficiently optimizing over this design space with minimum number of expensive function calls [1]}, [2]}, [3]}. Despite the success of continuous Bayesian optimization strategies, multi-objective, mixed-variable problems remain an area of open research. The inherent continuous nature of GP makes dealing with mixed-variable problems challenging. Finding a Pareto-front for multi-objective problems, and parallelizing function calls for batch updates also remain as challenges in the sequential setting of the BO algorithm. `Hedge' strategies for acquisition functions have proven to be very efficient for BO for continuous design spaces, however, Hedge strategies have not been formulated for multi-objective and mixed-variable problems.
